Identifying uncertain classifications

ABSTRACT

An improvement of the functionality of a computerized automatic recommendation engine is provided. In particular, a method for identifying uncertain classifications made by a computerized recommendation engine through the utilization of historical solution data, such that they can be flagged for subsequent human review, thereby improving the training process for the recommendation engine, is disclosed.

NOTICE OF COPYRIGHTS AND TRADE DRESS

A portion of the disclosure of this patent document contains materialwhich is subject to copyright or trade dress protection. This patentdocument may show and/or describe matter that is or may become tradedress of the owner. The copyright and trade dress owner has no objectionto the facsimile reproduction by anyone of the patent disclosure, as itappears in the Patent and Trademark Office patent files or records, butotherwise reserves all copyright and trade dress rights whatsoever.

CLAIM OF PRIORITY

This application claims the benefit of Indian Application No.202041020016, filed on May 21, 2020, the content of which is herebyincorporated by reference in its entirety.

FIELD OF THE EMBODIMENTS

The present disclosure relates generally to improvement of thefunctionality of a computerized automatic recommendation engine. Inparticular, the present disclosure and the embodiments contained thereinrelate to a method for improving the resolution of queries ticketsthrough the utilization of historical solution data obtained frommultiple sources, by providing a methodology for automaticallyidentifying uncertain classifications and recommendations such that theycan be flagged for subsequent human review, thereby improving thetraining process.

BACKGROUND

Machine learning-based recommendation engines or recommendation systemsoffer a dramatic improvement over the old ways of doing things. By beingable to selectively iterate through massive amount of data whileperforming a variety of similarity calculations, these systems are ableto quickly and accurately provide known answers or potential answerscontained within an attached database to an end-user.

However, while these systems are excellent at providing answers when thesystem is confident that the answer is correct, these systems strugglegreatly at identifying recommendations having an uncertain level ofconfidence. That is, these systems are good at determining a known“good” answer or a known “bad” answer, but struggle with handlingrecommendations that do not fall neatly into those categories.

As the amount of information contained in databases attached torecommendations continues to grow, having a human identify all ofuncertain recommendations within the database is impractical, if notimpossible. As such, there is a need for a method that allows acomputerized recommendation system to automatically determine whichrecommendations contained within an attached database have an uncertainlevel of confidence, so that they may be forwarded to a human for manualreview, ultimately improving the performance of the recommendationengine.

SUMMARY

An aspect of an example embodiment in the present disclosure is toprovide a method for identifying inaccurate classifications made by acomputerized recommendation engine. The method begins by providing aplurality of classes in electronic format, where the classes arecontained within the recommendation engine, as well as providing a setof data points, wherein each data point is transformed into amulti-dimensional vector and wherein each data point contains asolution. From there, a query in electronic format is provided, wherethe query is to be processed by the computerized recommendation engine.The computerized recommendation engine is then utilized to provide asubset of data points, selected from the set of datapoints, where thesubset of data points is created by performing a similarity calculationon each data point in the set of data points and selecting data pointswith the highest similarity above a predetermined threshold. An averagesimilarity of the subset of data points compared to the query is thencomputed, along with a range of entropy in the subset of data points. Acomposite score of all data points within the subset of data points issubsequently computed by taking the division product of the range ofentropy over the average similarity. Preferably, the range of entropy isfrom 0 to 1, inclusive. A maximum threshold and a minimum threshold aredetermined. Then, a confidence value based on the composite score of thedata points within the subset of data points, where the confidence valueindicates whether the classification is inaccurate is calculated.Finally, a given classification is identified as being inaccurate if theconfidence value is between the minimum threshold and the maximumthreshold.

In a preferred embodiment, the maximum threshold is determined byretrieving a confidence value for each data point in the set of datapoints, then separating the data points into a first sequence and asecond sequence, separating the data points into a first sequence and asecond sequence, wherein the first sequence contains data points thathave been marked by a human user as being a known accurate answer,wherein the second sequence contains data points that have been markedby the human user as a known inaccurate answer;

A first mean and first standard deviation for the first sequence arecomputed, as well as a second mean and a second standard deviation forthe second sequence. From there an optimal value of a hyperparameter isdetermined, and the maximum threshold is determined as the second meansubtracted from the product of the optimal value of the hyperparameterand the second standard deviation.

In another preferred embodiment, the minimum threshold is determined bytaking the first mean subtracted from the product of the optimal valueof the hyperparameter and the first standard deviation. Preferably, theoptimal value of the hyperparameter is less than the second mean minusthe first mean, divided by the sum of the first standard deviation andthe second standard deviation.

The present disclosure addresses at least one of the foregoingdisadvantages. However, it is contemplated that the present disclosuremay prove useful in addressing other problems and deficiencies in anumber of technical areas. Therefore, the claims should not necessarilybe construed as limited to addressing any of the particular problems ordeficiencies discussed hereinabove. To the accomplishment of the above,this disclosure may be embodied in the form illustrated in theaccompanying drawings. Attention is called to the fact, however, thatthe drawings are illustrative only. Variations are contemplated as beingpart of the disclosure.

Implementations may include one or a combination of any two or more ofthe aforementioned features.

These and other aspects, features, implementations, and advantages canbe expressed as methods, apparatuses, systems, components, programproducts, business methods, and means or steps for performing functions,or some combination thereof.

Other features, aspects, implementations, and advantages will becomeapparent from the descriptions, the drawings, and the claims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure provides for a method for automaticallyidentifying uncertain recommendations and classifications thereof madeby a computerized recommendation engine. While the methodology containedherein may be used with a variety of artificial intelligence, machinelearning, and recommendation engine system, said methodology is highlybeneficial when applied to the technologies contained in U.S. patentapplication Ser. No. 16/634,656, the contents of which are herebyincorporated by reference in their entirety.

The method in accordance with the present disclosure begins by providinga plurality of classes in electronic format and a set of data pointswhere each data point contained an answer, potential answer, or asolution and is transformed into a multi-dimension vector. The pluralityof classes can be expressed by C={C_(i): i∈[1, |C|]} and the set of datapoints can be expressed by D={x_(i): i∈[1, N], x_(i)∈R^(d)}, wherex_(i)∈R^(d) is a data point in d-dimensional space. A query, inelectronic format, is then provided to the recommendation engine wherethe query has at least one field. The recommendation engine will thenprovide a subset of data points selected from the set of data points byperforming a similarity calculation on each data point in the set ofdata points against the query and selecting the data points with theirsimilarity calculation being above a predetermined threshold.

In a preferred embodiment, the similarity calculation is performed bythe recommendation engine generating a plurality of inverted indices forthe at least one field, and then extending the dimensions of the queryare extended. A first similarity measure is then calculated for each ofthe plurality of inverted indices against each of the data points. Therecommendation then joins each of the data points when their respectivefirst similarity measure is above a threshold amount. The data pointsare then each extended to match the dimensions of the extendeddimensions of the query. The recommendation engine then performs asecond similarity measure of extended dimensions of the query with eachfield of the joined data points. Finally, the recommendation engine thenprovides the top x results of the data points based on a composite ofthe first similarity measure and the second similarity measure, sampledfrom the top (x*a) results of the plurality of potential answers, wherea is an integer multiple of x.

For any given data point, x∈R^(d), the recommendation engine will reportthe probabilities of the classes in the set of classes C as aprobability vector {circumflex over (p)}={p₁, p₂, . . . , p_(|C|)}, suchthat, Σ_(i=1) ^(|C|)p_(i)=1. After the subset of similarity measures forthe data points {s₁, . . . s_(K)} has been generated, an averagesimilarity of the subset is computed using the following equation:

s

=Σ_(i=1) ^(K)p_(i)*s_(i).

A range of entropy for the subset of data points is then defined. Todetermine the range of entropy in the subset of data points, the entropyof the subset may be calculated as E=−Σ_(i=1) ^(|C|)p_(i)*ln (p_(i)),which would result in the maximum entropy E_(max), being equal to ln(|C|). Thus, the range of entropy will be defined as

${E_{range} = \frac{E}{E_{\max}}},{E_{range}{{\epsilon\left\lbrack {0,1} \right\rbrack}.}}$A composite score of all the data points within the subset is thencalculated by dividing the range of entropy E_(range) by the averagesimilarity

s

.

From there a maximum threshold, θ_(bad) and minimum threshold, θ_(good)must be determined. Preferably, these thresholds are determinedautomatically from the historical data contained in the database of therecommendation engine. To do this, a hyperparameter, k must bedetermined. To determine 1K, the data points from the set of historicaldata points must have a label retrieved, where the label indicateswhether the data point provided an accurate solution or an inaccuratesolution. The data points are then split into two groups, or sequences,one group containing all of the data points that have been marked by ahuman user as being a known accurate answer, the second group containingall of the data points that have been marked by a human user as being aknown inaccurate answer. A first mean μ_(good), and a first standarddeviation σ_(good) are prepared from taking the mean and standarddeviation of the composite scores of the first group, and a second meanμ_(bad) and a second standard deviation σ_(bad) are prepared from takingthe mean and standard deviation of the composite scores of the secondgroup. With these values defined, the hyper parameter k results in thefollowing inequality is created

$k < \frac{\mu_{bad} - \mu_{good}}{\sigma_{bad} + \sigma_{good}}$and a limit of the subset is defined as

${LT} = {\frac{\mu_{bad} - \mu_{good}}{\sigma_{bad} + \sigma_{good}}.}$To determine the optimal value of the hyperparameter k_(opt), thefollowing chart is used:

LT k_(opt) ≥3 3 [≥2, <3] 2 [≥0, <2] 3

Once the optimal hyperparameter has been determined, the recommendationengine tests whether the following inequality is true:(μ_(good)+k_(opt)*σ_(good))<(μ_(bad)−k_(opt)*σ_(bad)). If this is truethen θ_(good)=(μ_(good)+k_(opt)*σ_(good)) andθ_(bad)=(μ_(bad)−k_(opt)*σ_(bad)). If the inequality is not true, thensubset of data points must be expanded until the inequality becomestrue. If the subset of data points cannot be expanded to make theinequality true, the method fails, and an error message is returned tothe end user.

After the maximum threshold and minimum thresholds have been determined,a confidence value is assigned to the subset. If the composite score isin between the minimum threshold and the maximum threshold, you know theanswer, potential answer, or classification provided by therecommendation engine cannot be trusted and must be forwarded to a humanuser for review and manual marking.

It is further understood that, although ordinal terms, such as, “first,”“second,” “third,” are used herein to describe various elements,components, regions, layers and/or sections, these elements, components,regions, layers and/or sections should not be limited by these terms.These terms are only used to distinguish one element, component, region,layer and/or section from another element, component, region, layerand/or section. Thus, “a first element,” “component,” “region,” “layer”and/or “section” discussed below could be termed a second element,component, region, layer and/or section without departing from theteachings herein.

Features illustrated or described as part of one embodiment can be usedwith another embodiment and such variations come within the scope of theappended claims and their equivalents.

The invention is described above with reference to block and flowdiagrams of systems, methods, apparatuses, and/or computer programproducts according to exemplary embodiments of the invention. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented or may not necessarily need to be performed at all, accordingto some embodiments of the invention.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks. As an example, embodiments of the invention may provide for acomputer program product, comprising a computer-usable medium having acomputer-readable program code or program instructions embodied therein,said computer-readable program code adapted to be executed to implementone or more functions specified in the flow diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational elements or steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide elements or steps for implementing the functionsspecified in the flow diagram block or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specified functionsand program instruction means for performing the specified functions. Itwill also be understood that each block of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, can be implemented by special-purpose, hardware-based computersystems that perform the specified functions, elements or steps, orcombinations of special purpose hardware and computer instructions.

As the invention has been described in connection with what is presentlyconsidered to be the most practical and various embodiments, it is to beunderstood that the invention is not to be limited to the disclosedembodiments, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the scope ofthe appended claims. Although specific terms are employed herein, theyare used in a generic and descriptive sense only and not for purposes oflimitation.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined in the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

In conclusion, herein is the present disclosure that relates generallyto improvement of the functionality of a computerized automaticrecommendation engine. In particular, the present disclosure and theembodiments contained therein relate to a method for improving theresolution of queries tickets through the utilization of historicalsolution data obtained from multiple sources, by providing a methodologyfor automatically identifying uncertain classifications andrecommendations such that they can be flagged for subsequent humanreview, thereby improving the training process.

The disclosure is illustrated by example in the drawing figures, andthroughout the written description. It should be understood thatnumerous variations are possible, while adhering to the inventiveconcept. Such variations are contemplated as being a part of the presentdisclosure.

What is claimed is:
 1. A method for identifying uncertainclassifications made by a computerized recommendation engine, comprisingthe steps of: providing a plurality of classes in electronic format madeby an artificial intelligence-based computerized recommendation engine;providing a set of data points, wherein each data point is transformedinto a multi-dimensional vector and wherein each data point contains asolution; providing a query in electronic format to be processed by theartificial intelligence-based computerized recommendation engine;utilizing the artificial intelligence-based computerized recommendationengine to provide a subset of data points, selected from the set ofdatapoints, wherein the subset of data points is created by performing asimilarity calculation on each data point in the set of data points andselecting data points with the highest similarity compared to the query,above a predetermined threshold; computing an average similarity of thesubset of data points compared to the query; computing a range ofentropy in the subset of data points; computing a composite score of alldata points within the subset of data points by taking the divisionproduct of the range of entropy over the average similarity; determininga maximum threshold for a confidence value; determining a minimumthreshold for the confidence value; and assigning the confidence valuebased on the composite score of the data points within the subset ofdata points, wherein the confidence value indicates whether aclassification to a class is inaccurate; identifying a givenclassification to a class made by the artificial intelligence-basedrecommendation engine as being inaccurate when the confidence value isbetween the minimum threshold and the maximum threshold.
 2. The methodof claim 1, the step of “determining a maximum threshold for aconfidence value” comprises the steps of: retrieving a historicalconfidence value for each data point in a set of historical data points;separating the data points in the set of historical data points into afirst sequence and a second sequence, wherein the first sequencecontains data points that have been marked by a human user as being aknown accurate answer, wherein the second sequence contains data pointsthat have been marked by the human user as a known inaccurate answer;computing a first mean and a first standard deviation for the historicalconfidence values in the first sequence; computing a second mean and asecond standard deviation for the historical confidence values in thesecond sequence; determining an optimal value of a hyperparameter; anddetermining the maximum threshold for a confidence value by subtractingthe product of the optimal value of the hyperparameter and the secondstandard deviation from the second mean.
 3. The method of claim 2, thestep of “determining a minimum threshold for the confidence value”comprises the step of: determining the minimum threshold for theconfidence value as the first mean added to the product of the optimalvalue of the hyperparameter and the first standard deviation.
 4. Themethod of claim 3, wherein the optimal value of the hyperparameter isless than the second mean minus the first mean, divided by the sum ofthe first standard deviation and the second standard deviation.
 5. Themethod of claim 4, wherein the range of entropy is between 0 and 1,inclusive.
 6. A method for identifying uncertain classifications made bya computerized recommendation engine, comprising the steps of: providinga plurality of classes in electronic format made by an artificialintelligence-based computerized recommendation engine; providing a setof data points, wherein each data point is transformed into amulti-dimensional vector and wherein each data point contains asolution; providing a query in electronic format to be processed by theartificial intelligence-based computerized recommendation engine;utilizing the artificial intelligence-based computerized recommendationengine to provide a subset of data points, selected from the set ofdatapoints, wherein the subset of data points is created by performing asimilarity calculation on each data point in the set of data points andselecting data points with the highest similarity compared to the query,above a predetermined threshold; computing an average similarity of thesubset of data points compared to the query; computing a range ofentropy in the subset of data points; computing a composite score of alldata points within the subset of data points by taking the divisionproduct of the range of entropy over the average similarity; determininga maximum threshold for a confidence value; determining a minimumthreshold for the confidence value; and assigning the confidence valuebased on the composite score of the data points within the subset ofdata points, wherein the confidence value indicates whether aclassification to a class is inaccurate; and identifying a givenclassification to a class made by the artificial intelligence-basedrecommendation engine as being inaccurate when the confidence value isbetween the minimum threshold and the maximum threshold wherein the stepof determining a maximum threshold for a confidence value comprises thesteps of: retrieving a historical confidence value for each data pointin a set of historical data points; separating the data points in theset of historical data points into a first sequence and a secondsequence, wherein the first sequence contains data points that have beenmarked by a human user as being a known accurate answer, wherein thesecond sequence contains data points that have been marked by the humanuser as a known inaccurate answer; computing a first mean and a firststandard deviation for the historical confidence values in the firstsequence; computing a second mean and a second standard deviation forthe historical confidence values in the second sequence; determining anoptimal value of a hyperparameter; and determining the maximum thresholdfor a confidence value by subtracting the product of the optimal valueof the hyperparameter and the second standard deviation from the secondmean, and wherein the step of determining a minimum threshold for theconfidence value comprises the step of: determining the minimumthreshold for the confidence value as the first mean added to theproduct of the optimal value of the hyperparameter and the firststandard deviation.